Next week, a virtual conference will feature a presentation by Redica Systems Senior GMP Quality Expert Jerry Chapman, one of our Industry Experts.

Jerry took a few minutes to answer some questions about his talk.

Conference Details

Date: Wednesday, April 14
Event: Society of Quality Assurance Annual Meeting
Presentation by: Redica Systems Senior GMP Quality Expert Jerry Chapman
Presentation: “Enabling Quality and Regulatory Professionals with Structured Data and Actionable Insights Using AI”
Session: “Session Z – GMP”
Time: Begins at 3:30 p.m. EST

Interview with Jerry

Can you tell us more about how GMP quality leaders can take advantage of data analytics?

Jerry Chapman

Jerry: The use of data adds credibility and predictability to proposals and creates more reliable predictors of outcomes. Specifically regarding GMP inspections, what is even more important than the rules, regulations, and guidance from regulatory agencies is how they are enforced during manufacturing site inspections.

In other words, what do FDA and other agencies expect to see when inspecting a production facility? Those expectations are difficult to find and always changing.  The only place to find out what is expected as current—the “C” in CGMP—is by evaluating enforcement reports such as FDA 483s and warning letters and looking for trends and commonalities in inspection reports for products and processes similar to the ones in your facility.  That exercise is time-consuming and difficult and requires expertise that is not easy to come by.

At Redica Systems, we enable that analysis using our proprietary algorithms.

[Related: Gain access to the same data Jerry used for his presentation with a free trial of the Redica Systems platform.]

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If I am a quality leader interested in learning more about AI and data analytics, where should I start?  What resources do you recommend for someone who has experience in GMP but wants to learn more about AI and data analytics?

Jerry: Redica Systems has built the largest integrated dataset of inspection and enforcement documents in the world.  These are the datasets I use in my presentation for the case study analyses.  Our data library includes all FDA inspected and registered sites since 2000, comprising 20 times more data than has been released on FDA.gov, which we have obtained by targeted Freedom of Information (FOI) requests.

But once you get the inspection data, do you have the experts to analyze it?  And if so, do they still print hard copies and use highlighting markers to analyze them?  Redica Systems has better ways to do this. We have built and implemented proprietary algorithms using machine learning and honed by industry experts.  These expert models evaluate inspection documents the way an expert would, revealing not only obvious agency findings but also issues that experts would see, “hiding in plain sight.”

In my presentation, I provided an introduction to machine learning and natural language processing tools, so hearing the presentation is a great place to start for anyone wanting to learn more about AI and data analytics. It includes two case studies that are very instructive.

What is an example of an enforcement trend you’ve found from analyzing data?

Jerry: In an analysis of the previous six years of warning letters (excluding 2020, which is not representative), we used our proprietary algorithm to search for citations indicating issues with data integrity.  Data integrity comprises a myriad of scenarios and is rarely cited directly but is cited in the area where it is found.  As I show in one of the case studies in the presentation, we examined a population of over 200 drug GMP warning letters and found that about 68% of them had data integrity issues. Surprisingly, as a percentage of total warning letters by country, the percentage of warning letters including language pointing to data integrity concerns was as high or higher in developed countries like Canada and the United States than in India and China, countries that have been in the news for issues in those areas.

Our ability to examine large data sets with what we call our “expert models”—computer models built and trained by industry experts—has produced some surprises and we anticipate will continue to do so.

How will new technologies such as big data, machine learning, and AI shape the future of quality and compliance in GMP?

Jerry: The number of health authorities inspecting U.S. companies continues to increase as do the number of regulations and guidance documents they inspect against.  As our industry continues to become more global and complex with intricate international supply chains and changing regulatory requirements in countries where firms are marketing their products, it becomes more and more difficult to keep current with regulatory agency expectations.  The only way to ensure you stay up-to-date is by understanding and leveraging large datasets and the kinds of accompanying analytics that Redica Systems has available.

The trend in the pharmaceutical industry is to move toward what has been called “pharma 4.0.”  This encompasses an advanced business model for mature pharma organizations in which digitalization of all records and control strategies and enablers, data integrity “by design,” and the embodiment of the Pharmaceutical Quality System as described in ICH Q10, among other features, are key.

This type of model has proven to be a successful operating model in other industries.  A hallmark includes interconnected systems and data analytics used for decision-making.  The ability to bring large amounts of information to bear on strategies as they are being designed and on problems as they emerge will be critical.  This includes the use of “big data”—datasets that are too large or complex to be dealt with by traditional data-processing methods.

Redica Systems enables pharma, medical device and food manufacturers to use our unmatched integrated data libraries and AI to reduce risk in large manufacturing environments and ensure regulatory compliance.

We facilitate pharma, medical device and food manufacturers’ usage of large data sets and AI to reduce risk in large manufacturing environments and ensure regulatory compliance.  The company’s unique platform provides customers with insights to continuously improve compliance, reduce supplier risk and increase team efficiency.

We have worked closely with our customers in order to shape Redica’s strategic focus, which involves using actionable data intelligence to build up to an entirely new way of thinking about, simplifying, and guiding regulatory compliance.

[Related: Gain access to the same data Jerry used for his presentation with a free trial of the Redica Systems platform.]

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Quality Management Systems for Small Pharma and Biotech Firms

Webinar: QMS for Small Pharma and Biotech Firms

Presented on Tuesday, March 30, 2021
by Fenton Fong, Founder, Managing Director, & Principal at xCellarate

Download the slide deck and video of this informative 1-hour webinar on the common challenges small pharma firms face when developing a QMS.

What you will learn:

  • Common challenges small pharma firms face when developing a QMS
  • Recent enforcement trends involving the Quality Unit
  • Impact of the COVID-19 pandemic

Download the Special Report

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In this summary we identify:

GMP data from FY2017 to FY2020 for the Asia-Pacific region

Top 3 GMP primary and secondary citations

Top 3 483 observations

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An Analysis Of MHRA’s Latest Annual GMP Inspection Deficiencies Report

In this summary we identify:

  • Data from 2019 UK MHRA Inspections
  • Trends from 2015, 2016, 2018, and 2019 inspections
  • Conclusions drawn from an analysis of drug inspection data

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SITES Quickly assess inspection records including dates and inspectors across all of your sites

INSPECTIONS Complete list of FDA inspections by date, company, category, and country

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